Image classification, including object recognition within images, plays an important role in various applications including content based image retrieval (CIBR) and computer vision applications. Commonly, image searches make use of text associated with images. Search results can be improved by taking into account visual information contained in the images themselves. Several CBIR methods make use of classifiers trained on image search results, to refine the search.
Traditionally, classifiers are trained using sets of images that are labeled by hand. Collecting such a set of images is often a very time-consuming and laborious process. Moreover, such images sets are relatively small and do not fully capture the breadth and variability of images encountered in practice. Category level object recognition needs to recognize objects under different illuminations, positions, and backgrounds; object instances can be very diverse. A large and diverse set of training images is desirable.
Internet search engines (e.g. Google image search) and websites containing large collections of images can potentially be used as a source of images to create larger training sets for image classifiers. Unfortunately, images from such sources are often not accurately labeled and search results can contain a high percentage of unrelated images within the results. It has been estimated that, when a search engine such as Google images is queried with the name of an object category, up to 85% of the returned images are unrelated to the category. Manually such editing large image collections to improve the quality of training images selected from such sources may be too expensive and time consuming to be practical. Improved methods for automatically selecting training images are needed.